Source: NOAA’s Climate Divisional Database (nClimDiv), June 2021 release
The Climate Divisional Dataset from the National Center For Environmental Information began as the only long-term temporally and spatially complete dataset from which to generate historical climate analyses (1895-2013) for the contiguous United States (CONUS). It was originally developed for climate division, statewide, regional, national, and population-weighted monitoring of drought, temperature, precipitation, and heating/cooling degree day values.
There are 344 climate divisions in the CONUS. For each climate division, monthly station temperature and precipitation values are computed from the daily observations. The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. (Karl and Koss, 1984).
The data here is the county data from all the states in the contigous United States. While NOAA includes information on a variety of climate measures, we are focusing on the maximum, minimum temperature, and precipitation of the counties of interest.
To Learn More, See:
glimpse(eastern_sf1)
## Rows: 252
## Columns: 49
## $ NAME <chr> "Accomack", "Accomack", "Accomack", "Accomack", "Accomack"…
## $ Year <dbl> 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904…
## $ CNTY_FIPS <chr> "001", "001", "001", "001", "001", "001", "001", "001", "0…
## $ X <dbl> -75.66484, -75.66484, -75.66484, -75.66484, -75.66484, -75…
## $ Y <dbl> 37.76368, 37.76368, 37.76368, 37.76368, 37.76368, 37.76368…
## $ STATE_FIPS <chr> "51", "51", "51", "51", "51", "51", "51", "51", "51", "51"…
## $ FIPS <chr> "51001", "51001", "51001", "51001", "51001", "51001", "510…
## $ AREA <dbl> 415.34, 415.34, 415.34, 415.34, 415.34, 415.34, 415.34, 41…
## $ Janmin <dbl> 27.5, 28.0, 26.2, 32.1, 27.8, 29.1, 30.0, 25.8, 28.2, 23.1…
## $ Febmin <dbl> 19.5, 29.9, 31.4, 28.6, 22.3, 26.9, 23.5, 25.1, 30.9, 23.0…
## $ Marmin <dbl> 34.2, 32.6, 38.8, 40.1, 35.9, 32.5, 38.0, 37.8, 41.9, 35.8…
## $ Aprmin <dbl> 45.0, 48.1, 43.9, 43.0, 42.4, 44.8, 42.7, 43.7, 46.3, 43.2…
## $ Maymin <dbl> 53.2, 60.6, 53.0, 55.6, 54.1, 54.6, 54.1, 55.1, 55.7, 55.4…
## $ Junmin <dbl> 65.8, 65.1, 62.4, 64.3, 66.0, 65.9, 64.3, 63.3, 61.7, 64.4…
## $ Julmin <dbl> 66.8, 70.8, 71.0, 71.8, 69.3, 71.3, 73.5, 68.6, 69.3, 68.4…
## $ Augmin <dbl> 69.9, 68.7, 67.8, 72.0, 68.7, 71.6, 70.5, 65.2, 68.0, 67.4…
## $ Sepmin <dbl> 65.6, 62.0, 60.2, 65.4, 59.8, 66.9, 63.0, 61.6, 62.2, 62.1…
## $ Octmin <dbl> 45.3, 48.9, 53.9, 54.1, 52.6, 57.0, 49.3, 54.0, 50.1, 47.0…
## $ Novmin <dbl> 40.6, 45.1, 41.2, 39.6, 37.2, 44.3, 34.9, 45.5, 34.0, 36.5…
## $ Decmin <dbl> 32.8, 30.2, 33.9, 31.4, 31.6, 30.5, 29.7, 30.3, 26.0, 26.5…
## $ Avg_Tempmin <dbl> 47.18333, 49.16667, 48.64167, 49.83333, 47.30833, 49.61667…
## $ Janmax <dbl> 43.1, 42.8, 42.1, 48.1, 46.3, 46.0, 43.8, 41.3, 44.2, 42.7…
## $ Febmax <dbl> 34.1, 45.9, 45.4, 46.8, 37.5, 45.2, 38.6, 37.7, 48.6, 38.6…
## $ Marmax <dbl> 49.5, 48.9, 55.7, 57.9, 51.4, 48.4, 54.2, 55.3, 58.6, 50.8…
## $ Aprmax <dbl> 61.3, 63.7, 63.1, 58.3, 63.1, 60.9, 55.9, 60.1, 61.4, 59.4…
## $ Maymax <dbl> 68.6, 75.8, 70.6, 70.1, 71.9, 73.6, 69.1, 71.9, 72.5, 73.4…
## $ Junmax <dbl> 81.8, 78.8, 76.7, 79.1, 83.2, 80.5, 79.8, 80.3, 74.0, 79.4…
## $ Julmax <dbl> 82.8, 85.9, 82.6, 83.7, 85.3, 87.5, 87.2, 85.6, 85.6, 83.0…
## $ Augmax <dbl> 87.4, 86.3, 82.4, 84.6, 82.2, 87.6, 83.3, 82.0, 80.6, 81.2…
## $ Sepmax <dbl> 83.0, 76.1, 80.2, 78.8, 77.3, 81.6, 77.5, 75.7, 76.6, 77.9…
## $ Octmax <dbl> 64.0, 64.4, 68.6, 69.7, 69.2, 70.9, 67.5, 69.6, 68.1, 65.9…
## $ Novmax <dbl> 57.8, 63.1, 59.9, 55.6, 58.4, 59.5, 50.4, 61.2, 55.5, 52.9…
## $ Decmax <dbl> 49.6, 46.2, 49.5, 50.0, 49.1, 47.5, 46.5, 46.4, 45.2, 41.2…
## $ Avg_Tempmax <dbl> 63.58333, 64.82500, 64.73333, 65.22500, 64.57500, 65.76667…
## $ Janpcp <dbl> 4.46, 1.36, 1.92, 1.63, 2.77, 2.52, 2.62, 3.04, 3.13, 1.60…
## $ Febpcp <dbl> 1.57, 6.62, 5.60, 1.53, 5.95, 3.93, 0.82, 5.22, 4.49, 3.40…
## $ Marpcp <dbl> 4.10, 2.66, 3.31, 4.47, 4.62, 3.15, 3.64, 1.95, 6.52, 3.30…
## $ Aprpcp <dbl> 6.34, 1.07, 2.94, 3.95, 1.68, 2.27, 4.14, 3.28, 3.04, 1.83…
## $ Maypcp <dbl> 3.93, 2.65, 2.86, 3.11, 1.84, 3.19, 2.90, 1.94, 1.25, 1.94…
## $ Junpcp <dbl> 2.57, 2.45, 2.21, 2.40, 3.06, 2.84, 3.75, 3.80, 4.00, 3.00…
## $ Julpcp <dbl> 3.25, 4.96, 8.26, 3.92, 5.39, 3.48, 3.79, 4.07, 4.68, 5.20…
## $ Augpcp <dbl> 2.05, 2.77, 2.97, 5.72, 4.29, 3.20, 6.91, 2.43, 4.04, 2.26…
## $ Seppcp <dbl> 2.50, 5.44, 1.12, 2.91, 1.51, 2.10, 4.31, 5.93, 3.35, 2.26…
## $ Octpcp <dbl> 2.80, 1.92, 4.55, 4.07, 4.61, 2.16, 1.37, 2.95, 5.51, 2.50…
## $ Novpcp <dbl> 2.30, 2.30, 1.81, 3.76, 0.98, 4.09, 1.78, 3.05, 1.69, 1.90…
## $ Decpcp <dbl> 1.93, 1.18, 3.96, 2.66, 1.04, 2.61, 4.49, 4.08, 2.76, 5.61…
## $ Avg_Temppcp <dbl> 3.150000, 2.948333, 3.459167, 3.344167, 3.145000, 2.961667…
## $ Fipsyear <dbl> 44001011895, 44001011896, 44001011897, 44001011898, 440010…
## $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((-75.32379 3..., MULTIPOLYGON …
Observations are county level estimates of…
5-number summaries of (non-missing) numeric variables (remove non-numeric observations)
noaa %>% select(-c(CNTY_FIPS, Fipsyear, Year)) %>%
select(where(~is.numeric(.x) && !is.na(.x))) %>%
as.data.frame() %>%
stargazer(., type = "text", title = "Summary Statistics", digits = 0,
summary.stat = c("mean", "sd", "min", "median", "max"))
##
## Summary Statistics
## ========================================
## Statistic Mean St. Dev. Min Median Max
## ----------------------------------------
## Janmin 29 4 18 29 41
## Febmin 30 4 17 30 37
## Marmin 37 3 26 37 47
## Aprmin 45 2 40 45.2 54
## Maymin 55 3 48 55 63
## Junmin 64 2 58 64 71
## Julmin 69 2 64 69 74
## Augmin 68 2 63 68 73
## Sepmin 62 3 56 62 70
## Octmin 52 3 44 52 60
## Novmin 41 3 33 40 50
## Decmin 33 4 22 33 46
## Avg_Tempmin 49 2 41 49 52
## Janmax 46 5 33 46 59
## Febmax 47 5 34 48 59
## Marmax 55 4 42 55 67
## Aprmax 64 3 55 64 72
## Maymax 73 3 67 73 81
## Junmax 81 2 73 81 88
## Julmax 86 2 81 86 91
## Augmax 84 2 78 84 89
## Sepmax 79 2 72 79 85
## Octmax 69 3 64 69 77
## Novmax 59 3 49 58 65
## Decmax 49 4 38 50 62
## Avg_Tempmax 66 2 59 66 70
## Janpcp 3 1 1 3 10
## Febpcp 3 1 0 3 8
## Marpcp 4 2 0 4 10
## Aprpcp 3 1 0 3 6
## Maypcp 3 2 0 3 8
## Junpcp 4 2 -10 3 9
## Julpcp 5 2 -10 4 13
## Augpcp 4 3 -10 4 12
## Seppcp 3 2 -10 3 13
## Octpcp 3 2 -10 3 10
## Novpcp 3 2 -10 2 9
## Decpcp 3 2 -10 3 8
## Avg_Temppcp 3 1 -5 3 5
## ----------------------------------------
eastern_sf1a <-
ggplot(eastern_sf1) +
geom_sf(aes(fill = Julmax), color = "black", alpha = .9, na.rm = TRUE) +
geom_text_repel(data = eastern_sf1, aes(X, Y, label = NAME), size = 4, nudge_x = 1, nudge_y = 0, fontface = "bold", hjust = 0.9) +
scale_fill_fermenter(palette = "YlOrRd", direction = 1, type = "seq", n.breaks = 7) +
theme_void() +
guides(fill =
guide_colourbar(title.position="top", title.hjust = 0.5,
barwidth = 1)
) +
labs(fill = "Temperature ", title = 'Year: {frame_time}',
caption = "Maximum Temperature in July for Eastern Shore Counties") +
transition_time(as.integer(Year)) +
ease_aes('linear')
animate(eastern_sf1a, fps = 1, detail = 1, nframes = 127)
meta %>%
filter(varname == "Julmax") %>%
select(about) %>%
as.list()
$about [1] “Monthly Divisional Temperature format (f7.2) Range of values -50.00 to 140.00 degrees Fahrenheit. Decimals retain a position in the 7-character field. Missing values in the latest year are indicated by -99.99. Missing values removed for visualization, resulting in no plots for 2021”
#select only the july temperature and year column
eastern_sf1_jl <- select(eastern_sf1, NAME, Year, Julmax)
#rename the temperature column
eastern_sf1_jl <- rename(eastern_sf1_jl, ta = Julmax)
#create a date column because stripes only works with format = date
eastern_sf1_jl <- mutate(eastern_sf1_jl, date = str_c(Year, "01-01", sep = "-") %>% ymd())
#Filter out each County
eastern_sf1_jla <- filter(eastern_sf1_jl, NAME == "Accomack")
eastern_sf1_jln <- filter(eastern_sf1_jl, NAME == "Northampton")
# Create a theme for the stripes image
theme_strip <- theme_minimal()+
theme(axis.text.y = element_blank(),
axis.line.y = element_blank(),
axis.title = element_blank(),
panel.grid.major = element_blank(),
legend.title = element_blank(),
axis.text.x = element_text(vjust = 3),
panel.grid.minor = element_blank(),
plot.title = element_text(size = 14, face = "bold")
)
col_strip <- brewer.pal(11, "RdBu")
ggplot(eastern_sf1_jla,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Accomack County July Maximum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
ggplot(eastern_sf1_jln,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Northampton County July Maximum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
eastern_sf1b <-
ggplot(eastern_sf1) +
geom_sf(aes(fill = Avg_Tempmax), color = "black", alpha = .9, na.rm = TRUE) +
geom_text_repel(data = eastern_sf1, aes(X, Y, label = NAME), size = 4, nudge_x = 1, nudge_y = 0, fontface = "bold", hjust = 0.9) +
scale_fill_fermenter(palette = "BuPu", direction = 1, type = "seq", n.breaks = 6) +
theme_void() +
guides(fill =
guide_colourbar(title.position="top", title.hjust = 0.5,
barwidth = 1)
) +
labs(fill = "Temperature ", title = 'Year: {frame_time}',
caption = "Average Yearly Maximum Temperature For Eastern Shore Counties") +
transition_time(as.integer(Year)) +
ease_aes('linear')
animate(eastern_sf1b, fps = 1, detail = 1, nframes = 127)
meta %>%
filter(varname == "Avg_Tempmax") %>%
select(about) %>%
as.list()
$about [1] “Average yearly maximum temperature for each county. Created by suming the maximum temperature for each month, for each county, and taking the mean. NA’s for months in 2021 not currently available were removed and the mean calculated for the 6 months so far. Missing values removed for visualization, resulting in no plots for 2021”
#select only the annual temperature and year column
eastern_sf1_yr <- select(eastern_sf1, NAME, Year, Avg_Tempmax)
#rename the temperature column
eastern_sf1_yr <- rename(eastern_sf1_yr, ta = Avg_Tempmax)
#create a date column because stripes only works with format = date
eastern_sf1_yr <- mutate(eastern_sf1_yr, date = str_c(Year, "01-01", sep = "-") %>% ymd())
#Filter out each County. No need to do theme again it's already set
eastern_sf1_yra <- filter(eastern_sf1_yr, NAME == "Accomack")
eastern_sf1_yrn <- filter(eastern_sf1_yr, NAME == "Northampton")
ggplot(eastern_sf1_yra,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Accomack County Average Yearly Maximum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
ggplot(eastern_sf1_yrn,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Northampton County Average Yearly Maximum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
eastern_sf2a <-
ggplot(eastern_sf1) +
geom_sf(aes(fill = Julmin), color = "black", alpha = .9, na.rm = TRUE) +
geom_text_repel(data = eastern_sf1, aes(X, Y, label = NAME), size = 4, nudge_x = 1, nudge_y = 0, fontface = "bold", hjust = 0.9) +
scale_fill_fermenter(palette = "YlGnBu", direction = 1, type = "seq", n.breaks = 7) +
theme_void() +
guides(fill =
guide_colourbar(title.position="top", title.hjust = 0.5,
barwidth = 1)
) +
labs(fill = "Temperature ", title = 'Year: {frame_time}',
caption = "Minimum Temperature in July for Eastern Shore Counties") +
transition_time(as.integer(Year)) +
ease_aes('linear')
animate(eastern_sf2a, fps = 1, detail = 1, nframes = 127)
meta %>%
filter(varname == "Julmin") %>%
select(about) %>%
as.list()
$about [1] “Monthly Divisional Temperature format (f7.2) Range of values -50.00 to 140.00 degrees Fahrenheit. Decimals retain a position in the 7-character field. Missing values in the latest year are indicated by -99.99. Missing values removed for visualization, resulting in no plots for 2021”
ggplot(eastern_sf2_jla,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Accomack County July Minimum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
ggplot(eastern_sf2_jln,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Northampton County July Minimum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
eastern_sf2b <-
ggplot(eastern_sf1) +
geom_sf(aes(fill = Avg_Tempmin), color = "black", alpha = .9, na.rm = TRUE) +
geom_text_repel(data = eastern_sf1, aes(X, Y, label = NAME), size = 4, nudge_x = 1, nudge_y = 0, fontface = "bold", hjust = 0.9) +
scale_fill_fermenter(palette = "PuRd", direction = 1, type = "seq", n.breaks = 6) +
theme_void() +
guides(fill =
guide_colourbar(title.position="top", title.hjust = 0.5,
barwidth = 1)
) +
labs(fill = "Temperature ", title = 'Year: {frame_time}',
caption = "Average Yearly Minimum Temperature For Eastern Shore Counties") +
transition_time(as.integer(Year)) +
ease_aes('linear')
animate(eastern_sf2b, fps = 1, detail = 1, nframes = 127)
meta %>%
filter(varname == "Avg_Tempmin") %>%
select(about) %>%
as.list()
$about [1] “Average yearly minimum temperature for each county. Created by suming the minimum temperature for each month, for each county, and taking the mean. NA’s for months in 2021 not currently available were removed and the mean calculated for the 6 months so far. Missing values removed for visualization, resulting in no plots for 2021”
ggplot(eastern_sf2_yra,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Accomack County Average Yearly Minimum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
ggplot(eastern_sf2_yrn,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Northampton County Average Yearly Minimum Temperature 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
eastern_sf3a <-
ggplot(eastern_sf1) +
geom_sf(aes(fill = Julpcp), color = "black", alpha = .9, na.rm = TRUE) +
geom_text_repel(data = eastern_sf1, aes(X, Y, label = NAME), size = 4, nudge_x = 1, nudge_y = 0, fontface = "bold", hjust = 0.9) +
scale_fill_fermenter(palette = "PuBuGn", direction = 1, type = "seq", n.breaks = 7) +
theme_void() +
guides(fill =
guide_colourbar(title.position="top", title.hjust = 0.5,
barwidth = 1)
) +
labs(fill = "Precipitation", title = 'Year: {frame_time}',
caption = "Precipitation in July for Eastern Shore Counties") +
transition_time(as.integer(Year)) +
ease_aes('linear')
animate(eastern_sf3a, fps = 1, detail = 1, nframes = 127)
meta %>%
filter(varname == "Julpcp") %>%
select(about) %>%
as.list()
$about [1] “Monthly Divisional Temperature format (f7.2) Range of values -50.00 to 140.00 degrees Fahrenheit. Decimals retain a position in the 7-character field. Missing values in the latest year are indicated by -99.99. Missing values removed for visualization, resulting in no plots for 2021”
ggplot(eastern_sf3_jla,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Accomack County July Precipitation 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
ggplot(eastern_sf3_jln,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Northampton County July Precipitation 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
eastern_sf3b <-
ggplot(eastern_sf1) +
geom_sf(aes(fill = Avg_Temppcp), color = "black", alpha = .9, na.rm = TRUE) +
geom_text_repel(data = eastern_sf1, aes(X, Y, label = NAME), size = 4, nudge_x = 1, nudge_y = 0, fontface = "bold", hjust = 0.9) +
scale_fill_fermenter(palette = "RdPu", direction = 1, type = "seq", n.breaks = 6) +
theme_void() +
guides(fill =
guide_colourbar(title.position="top", title.hjust = 0.5,
barwidth = 1)
) +
labs(fill = "Precipitation", title = 'Year: {frame_time}',
caption = "Average Yearly Precipitation For Eastern Shore Counties") +
transition_time(as.integer(Year)) +
ease_aes('linear')
animate(eastern_sf3b, fps = 1, detail = 1, nframes = 127)
meta %>%
filter(varname == "Avg_Temppcp") %>%
select(about) %>%
as.list()
$about [1] “Average yearly precipitation for each county. Created by suming the precipitation for each month, for each county, and taking the mean. NA’s for months in 2021 not currently available were removed and the mean calculated for the 6 months so far. Missing values removed for visualization, resulting in no plots for 2021”
ggplot(eastern_sf3_yra,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Accomack County Average Yearly Precipitation 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip
ggplot(eastern_sf3_yrn,
aes(x = date, y = 1, fill = ta))+
geom_tile()+
scale_x_date(date_breaks = "6 years",
date_labels = "%Y",
expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = rev(col_strip))+
guides(fill = guide_colorbar(barwidth = 1))+
labs(title = "Northampton County Average Yearly Precipitation 1895-2020",
caption = "Data: NOAA Surface Temperature Analysis")+
theme_strip